Title: Mining team characteristics to predict Wikipedia article quality

Authors: Grace Gimon Betancourt, Armando Segnini, Carlos Trabuco, Amira Rezgui and Nicolas Jullien (Télécom Bretagne)

Abstract: In this study, we were interested in studying which characteristics of virtual teams are good predictors for the quality of their production. The experiment involved obtaining the Spanish Wikipedia database dump and applying different data mining techniques sui- table for large data sets to label the whole set of articles according to their quality (comparing them with the Featured/Good Articles, or FA/GA). Then we created the attributes that describe the characteristics of the team who produced the articles and using decision tree methods, we obtained the most relevant characteristics of the teams that produced FA/GA. The team’s maximum efficiency and the total length of contribution are the most important predictors. This article contributes to the literature on virtual team organization.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.